The present disclosure relates to root cause diagnostics. More particularly, it relates to performing root cause diagnostics of machine and/or system faults using temporal data mining for fault data correlation.
In a manufacturing or other machine-based environment, faults often occur during normal operation of the machines. In order to maximize productivity and lower costs, it is important to quickly and accurately remedy faults, and return to a normal operating state. As faults occur over a period of time, the sequence of events surrounding each fault can be monitored and recorded in data series, including preconditions that lead to a fault, and post conditions that follow the fault.
In many event sequences, individual time-ordered events in the data series are associated with time durations. In many instances, the time durations may carry useful information. For example, in line status logs of manufacturing plants, the durations of various events carry important information. Looking to immediate preconditions before a fault occurs can be helpful to diagnose the cause of the fault, but may not accurately determine the root cause of the fault, as some faults are the result of a series of events. It would be beneficial to develop a process to extract useful information from temporal data in a manufacturing or machine-based environment to assist in fault diagnostics and root cause analysis for a series of faults or events. Accordingly, there is a need in the art for root cause diagnostics of machine and system faults using temporal data mining for fault data correlation.